The Industrial Ecology Open Science Project
Bibliographic record
Abstract
Industrial Ecology (IE) is the core science of data- and model-driven sustainability research. IE quantifies the environmental and social consequences of human activities by linking environmental, social and economic data into a consistent accounting and modeling framework. Topics of IE research range from analysing global material cycles, to identifying the impacts of products and technologies, to the estimation of the environmental footprints of countries and regions. Driven by the growing complexity of economic systems as well as advances in our understanding of environmental and social interactions, IE research becomes increasingly computational and data intensive. For example, typical Multi-Regional Input-Output databases for the calculation of environmental footprints now contain more than 10 000 country-sector pairs combined with up to 1000 specific social and environmental pressures, resulting in systems with up to 1 billion variables. These databases, however, currently form monolithic data-silos; a common, open e-infrastructure for analysing and sharing IE results is missing. Besides the issues common to most data heavy scientific disciplines (large data versioning, provenance tracking, common ontologies, ...) IE research faces two specific challenges: IE research relies on data published by non-scientific agencies (e.g. businesses, national statistical agencies, international economic databases). These data rarely come with unique identifiers or version control and might be updated/replaced/deleted without notification. Currently, the only way to incorporate these data into a reproducible workflow is to document the access date and, if possible, store a local snapshot of the data. Many of the data sources used by IE contain confidential data (e.g. sales data, tax data). This hinders the implementation of a fully open workflow, demanding access control to raw data. To what extend results derived from such data can follow Open Access and Open Data standards still needs to be determined. Recently, a Data Transparency Task Force (DTTF) was formed by the International Society of Industrial Ecology and has established mandatory minimum requirements for data transparency and accessibility for the Journal of Industrial Ecology publications. Now, the DTTF evolved into the Industrial Ecology Open Science (IEOS) project, a bottom-up initiative for facilitating FAIR IE data as well as procedural transparency (Open Source/Workflow). First activities of the IEOS include: outlining steps towards better reuse of sustainability research software establishing a central IE repository for gathering scattered data and software setup of a Zenodo community for preprints and data/software sharing developing common databases for gathering research outcomes in a consistent way building an ontology for data classification developing software frameworks which unifies access to sustainability data in different formats None of these activities are currently part of the European Open Science Cloud (EOSC) or the European Data Infrastructure (EDI); it can be argued that IE belongs to the long tail of scientific fields as defined by the EOSCpilot. The purpose of this presentation is to discuss opportunities and challenges for moving the bottom-up driven activities into the EOSC and find synergies between current efforts and the existing EDI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.247 | 0.163 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".